Within highly mechanised agricultural productions systems such as the Australian cotton industry, operational energy inputs represent a significant cost to growers. Overall, it has been estimated that machinery may contribute 40-50% of the cotton farm input costs. In this project, a framework to assess the operational energy inputs of various production systems and the relative performance of a grower within an adopted system is developed. This framework is later implemented and incorporated into a user-friendly energy assessment tool (a Betta version web-enabled online energy calculator, EnergyCalc).

EnergyCalc divides energy usage of cotton production into six broadly distinct processes, which includes fallow, planting, in-crop, irrigation, harvesting and post harvest. This enables both the total energy inputs and the energy usage of each production processes to be assessed. In addition to the default energy use data provided, the software also allows the user to enter their own site-specific data so that they can benchmark their performance with peer farmers and best practices to identify opportunities for reduced energy costs.

Seven case studies are presented. It is found that overall, the total energy inputs for these farms was significantly influenced by the management and operation methods adopted, and ranged from 3.7-15.2 GJ/ha of primary energy, at a cost of $80-310/ha and 275-1404 kg CO2 equivalent/ha greenhouse gas emissions. Among all the farming practices, irrigation water energy use is found to be the highest and is typically 40-60% of total energy costs (wherever water is pumped). Energy use of the harvesting operation is also significant, accounting for 20% of overall direct energy use. If a farmer moves from conventional tillage to minimum tillage, there is a potential saving of around 10% of the fuel used on the farm. Compared with cotton, energy used in the production of other irrigated crops on these farms is generally half of cotton. This is due to less intensive management required for these crops, leading to the lower number of farming operations (passes) carried out (generally about 10, in comparison with 17-18 for cotton) and reduced irrigation requirements.

The opportunities for further work are also identified. EnergyCalc is currently being populated with generalised performance data obtained from various sources which may not be specific and accurate to the Australian conditions. Opportunities therefore exist to further test and improve the accuracy of the model. Wide promotion and use of this tool is also critical. Conceptually, EnergyCalc may also be extended to other Australian rural industries to conduct on-farm energy audits and recommend strategies to reduce energy input costs. This will provide an opportunity for co-investment from these industries for continuing development of the tool.